A Non-intrusive, Non-obstructive, Versatile Venous Reservoir Blood Volume Sensor Based on Computer Vision for Clinical Cardiopulmonary Bypass

We present an automatic venous reservoir blood volume sensor system, based on computer vision, for clinical cardiopulmonary bypass. A camera connected to a computer reads the blood volume inside the venous reservoir in real time, using custom-developed algorithms and software. The system's performance in blood volume measurement has been characterized, achieving a mean absolute percentage error as small as 2.5%. Through comprehensive testing in a simulated clinical environment, the system has proven to be nonintrusive to blood, nonobstructive for operators, and versatile for different reservoir models.

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Publication Details

Journal
IEEE Sensors Letters
Published
2025-03-17
DOI
https://doi.org/10.1109/lsens.2025.3551948
Citations
1
Primary Topic
Hemodynamic Monitoring and Therapy
Type
article
Field-Weighted Citation Impact
2.01
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article

A Non-intrusive, Non-obstructive, Versatile Venous Reservoir Blood Volume Sensor Based on Computer Vision for Clinical Cardiopulmonary Bypass

Chan‐Jin Chung, Ryan Kaddis, Hao Jiang, Sean Murtha
1 citations
IEEE Sensors Letters
Hemodynamic Monitoring and Therapy
2.01
article

A Non-intrusive, Non-obstructive, Versatile Venous Reservoir Blood Volume Sensor Based on Computer Vision for Clinical Cardiopulmonary Bypass

Chan‐Jin Chung, Ryan Kaddis, Hao Jiang, Sean Murtha
article en
1 citations

Abstract

We present an automatic venous reservoir blood volume sensor system, based on computer vision, for clinical cardiopulmonary bypass. A camera connected to a computer reads the blood volume inside the venous reservoir in real time, using custom-developed algorithms and software. The system's performance in blood volume measurement has been characterized, achieving a mean absolute percentage error as small as 2.5%. Through comprehensive testing in a simulated clinical environment, the system has proven to be nonintrusive to blood, nonobstructive for operators, and versatile for different reservoir models.

IEEE Sensors LettersVol. 9(5)
Lawrence Technological University (US)
Good health and well-being
Openalex Percentile: Top 18%
Hemodynamic Monitoring and Therapy
2.01
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